The present invention relates generally to imaging systems, and more particularly to techniques for reconstructing scout or navigational images from a series of images, such as in a medical context, for use in identifying and examining specific images on which the navigational image is based, specific features within the images, and so forth.
In a field of imaging systems, and particularly in the medical imaging field, a wide range of techniques are employed for generating image data. In medical imaging, for example, various modalities serve to generate image data based upon specific physical properties of materials and their interaction with imaging systems. Such modalities include computed tomography (CT) systems, X-ray systems, magnetic resonance imaging (MRI) systems, positron emission tomography (PET) systems, ultrasound systems, and so forth. These systems are generally designed to collect image data in response to various radiation, stimuli or signal sources within a subject. The signals can then be filtered and manipulated to form a dataset from which a reconstructed image may be generated. It should be noted that while reference is made throughout the present discussion of modalities employed in the medical imaging field, these same and other modalities may be employed in a wide range of other fields, including baggage processing, human and non-human screening, seismography, meteorology, and so forth.
In certain imaging modalities a large datasets are generated which can be used to reconstruct a large number of images. For example, in CT imaging a source of X-ray radiation is rotated about a subject opposite a detector. As X-rays penetrate the subject at various angles of rotation, the detector generates resulting signals which are representative of the impacting radiation, attenuated or absorbed by various tissues within a subject. In a helical mode a table on which the subject is positioned is advanced through the scanner, resulting in a very large dataset which can then serve for reconstruction of many images or virtual slices through the subject.
With the advent of 8 and 16-slice CT scanners, the number of images acquired in a scanning sequence has increased dramatically. In previous systems, a study or examination sequence may have included some 100–200 images on the average. Such numbers are relatively manageable for a technician or radiologist, who must navigate through and inspect many images visually using various types of displays, such as stacked mode displays and cine displays. Such displays allow the viewer to view individual images sequentially on a viewer. With high-resolution acquisition hardware, studies with image counts in excess of 2000 are becoming routine, however. Even greater numbers may be obtainable in the future. Given the workload and productivity demands, and time pressures on radiologists, navigating through large image sets is no longer a trivial problem, simply due to the time required to page through such large numbers of images.
A further difficulty in managing large image sets arises from the sheer volume of the data involved in each study. Large image datasets are typically stored in digital form in a picture archive communications system or PACS, or some other digital storage medium. For viewing, the images of interest are typically then loaded from the PACS to a diagnostic workstation. Large datasets require significant bandwidth and result in significant delay in the transfer from the PACS archive to the diagnostic workstation, however. For large studies, a radiologist may not require all images, but, at present, there is no enhanced method for locating which images may be of interest in a study. Current approaches may include techniques for minimizing the time required to display a first image. However, such techniques do not actually address the need to navigate through the collection of images for features of interest.
In one current mode of navigation through large datasets, images are selected at particular locations, such as inferior or superior locations, typically corresponding to the foot and head, respectively, in the case of full body CT image datasets. From the selected location, images are traversed sequentially on an image-by-image basis through the entire dataset. This method is particularly challenging the radiologist, especially in the case of large image datasets where many images must be accessed and displayed for such traversing. While certain acquired images may be used for relatively crude scouting purposes, such as initial scout images which may be acquired in CT applications, the images are not an accurate mechanism to cross-reference to the large series of images which are subsequently acquired and which are not directly related to the earlier scout image data.
There is a need, therefore, for an improved technique for navigating through large image datasets. The technique would advantageously provide a reliable tool for identifying features of interest in specific applications, and for identifying specific image or sets of images in which the features may be viewed in greater detail.